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Blueprint For Application Performance Management: Page 2 of 3

Yet, managing those Web services by piecing together data from conventional point management products won't cut it. Polling individual devices for SNMP alerts can't provide sufficient information to control real-time process flows that by their nature are ephemeral. In short, guaranteeing the performance of tomorrow's distributed Web services applications won't be possible without monitoring and managing the entire application flow.

APM has other drivers, too. To extract additional value out of IT investments and improve customer experience, executives are looking at managing IT end to end through governance and process specifications, such as COBIT and ITIL. While these specifications are excellent for pulling together IT business process, they require tools to implement the ideas set out in them. APM closely aligns with ITIL because it postulates a unified system for analyzing application performance problems, notes Dennis Drogseth, VP at IT consulting and analyst firm Enterprise Management Associates.

In fact, APM aligns neatly with at least four of the 14 ITIL service operation activities, Sevcik says, ticking off Incident Management, Availability Management, Capacity Management, and Service Level Management. In short, APM can be viewed as the tool by which ITIL gets implemented in the network (see diagram below).

diagram: The APM Architecture


(click image for larger view)

BUILDING APM
The APM architecture is built on three elements that enable testing and incident investigation capabilities: data collectors, analysis engines, and reporting stations. These elements come together to build a set of tools that proactively monitor systems and resolve application problems. In some cases, problems are diagnosed through active synthetic transaction monitors, while others may require passive agent or agentless monitoring.

Synthetic transaction monitors measure application performance by simulating user activity using predefined transactions. They can identify many user-perceived performance problems, but often can't determine where the actual problem is occurring. What's more, they require unique programming for each application monitored. Perhaps their most important use is for reporting user experience data, which can be trendable over long periods and through application revisions. Such data can be extremely useful for reporting on IT's service-level agreements.